Hybrid Fine-Tuning Strategy for Few-Shot Classification
文献类型：期刊论文
| 作者 | Zhao, Lei1,2; Ou, Zhonghua1; Zhang, Lixun1; Li, Shuxiao2
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| 刊名 | COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE
 |
| 出版日期 | 2022-10-08 |
| 卷号 | 2022页码:12 |
| ISSN号 | 1687-5265 |
| DOI | 10.1155/2022/9620755 |
| 通讯作者 | Li, Shuxiao(shuxiao.li@ia.ac.cn) |
| 英文摘要 | Few-shot classification aims to enable the network to acquire the ability of feature extraction and label prediction for the target categories given a few numbers of labeled samples. Current few-shot classification methods focus on the pretraining stage while fine-tuning by experience or not at all. No fine-tuning or insufficient fine-tuning may get low accuracy for the given tasks, while excessive fine-tuning will lead to poor generalization for unseen samples. To solve the above problems, this study proposes a hybrid fine-tuning strategy (HFT), including a few-shot linear discriminant analysis module (FSLDA) and an adaptive fine-tuning module (AFT). FSLDA constructs the optimal linear classification function under the few-shot conditions to initialize the last fully connected layer parameters, which fully excavates the professional knowledge of the given tasks and guarantees the lower bound of the model accuracy. AFT adopts an adaptive fine-tuning termination rule to obtain the optimal training epochs to prevent the model from overfitting. AFT is also built on FSLDA and outputs the final optimum hybrid fine-tuning strategy for a given sample size and layer frozen policy. We conducted extensive experiments on mini-ImageNet and tiered-ImageNet to prove the effectiveness of our proposed method. It achieves consistent performance improvements compared to existing fine-tuning methods under different sample sizes, layer frozen policies, and few-shot classification frameworks. |
| 资助项目 | National Natural Science Foundation of China ; [U19B2033] ; [62076020] |
| WOS研究方向 | Mathematical & Computational Biology ; Neurosciences & Neurology |
| 语种 | 英语 |
| WOS记录号 | WOS:000884395500001 |
| 出版者 | HINDAWI LTD |
| 资助机构 | National Natural Science Foundation of China |
| 源URL | [http://ir.ia.ac.cn/handle/173211/51272]  |
| 专题 | 自动化研究所_复杂系统管理与控制国家重点实验室_机器人应用与理论组 |
| 通讯作者 | Li, Shuxiao |
| 作者单位 | 1.Univ Elect Sci & Technol China, Chengdu, Peoples R China 2.Chinese Acad Sci, Inst Automat, Beijing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhao, Lei,Ou, Zhonghua,Zhang, Lixun,et al. Hybrid Fine-Tuning Strategy for Few-Shot Classification[J]. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE,2022,2022:12. |
| APA | Zhao, Lei,Ou, Zhonghua,Zhang, Lixun,&Li, Shuxiao.(2022).Hybrid Fine-Tuning Strategy for Few-Shot Classification.COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE,2022,12. |
| MLA | Zhao, Lei,et al."Hybrid Fine-Tuning Strategy for Few-Shot Classification".COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022(2022):12. |
入库方式： OAI收割
来源：自动化研究所
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